"""
Module containing most of the utility functions for the binarycpython package
Functions here are mostly functions used in other classes/functions, or
useful functions for the user
Tasks:
- TODO: change all prints to verbose_prints
"""
import json
import os
import tempfile
import copy
import inspect
import sys
from io import StringIO
from typing import Union, Any
from collections import defaultdict
import h5py
import numpy as np
from binarycpython import _binary_c_bindings
import binarycpython.utils.moe_distefano_data as moe_distefano_data
import py_rinterpolate
########################################################
# Unsorted
########################################################
def convert_bytes(size):
"""
Function to return the size + a magnitude string
"""
for x in ['bytes', 'KB', 'MB', 'GB', 'TB']:
if size < 1024.0:
return "%3.1f %s" % (size, x)
size /= 1024.0
return size
def get_size(obj, seen=None):
"""
Recursively finds size of objects
From https://github.com/bosswissam/pysize
"""
size = sys.getsizeof(obj)
if seen is None:
seen = set()
obj_id = id(obj)
if obj_id in seen:
return 0
# Important mark as seen *before* entering recursion to gracefully handle
# self-referential objects
seen.add(obj_id)
if isinstance(obj, dict):
size += sum([get_size(v, seen) for v in obj.values()])
size += sum([get_size(k, seen) for k in obj.keys()])
elif hasattr(obj, '__dict__'):
size += get_size(obj.__dict__, seen)
elif hasattr(obj, '__iter__') and not isinstance(obj, (str, bytes, bytearray)):
size += sum([get_size(i, seen) for i in obj])
return size
def subtract_dicts(dict_1: dict, dict_2: dict) -> dict:
"""
Function to subtract two dictionaries.
Only allows values to be either a dict or a numerical type
For the overlapping keys (key name present in both dicts):
When the keys are of the same type:
- If the types are of numerical type: subtract the value at dict 2 from dict 1.
- If the types are both dictionaries: call this function with the subdicts
WHen the keys are not of the same type:
- if the keys are all of numerical types
For the unique keys:
- if the key is from dict 1: adds the value to the new dict (be it numerical value or dict)
- If the key is from dict 2: Adds the negative of its value in case of numerical type.
if the type is a dict, the result of subtract_dicts({}, dict_2[key]) will be set
If the result is 0, the key will be removed from the resulting dict.
If that results in an empty dict, the dict will be removed too.
Args:
dict_1: first dictionary
dict_2: second dictionary
Returns:
Subtracted dictionary
"""
# Set up new dict
new_dict = {}
# Define allowed numerical types
ALLOWED_NUMERICAL_TYPES = (float, int, np.float64)
#
keys_1 = dict_1.keys()
keys_2 = dict_2.keys()
# Find overlapping keys of both dicts
overlapping_keys = set(keys_1).intersection(set(keys_2))
# Find the keys that are unique
unique_to_dict_1 = set(keys_1).difference(set(keys_2))
unique_to_dict_2 = set(keys_2).difference(set(keys_1))
# Add the unique keys to the new dict
for key in unique_to_dict_1:
# If these items are numerical types
if isinstance(dict_1[key], ALLOWED_NUMERICAL_TYPES):
new_dict[key] = dict_1[key]
if new_dict[key] == 0:
del new_dict[key]
# Else, to be safe we should deepcopy them
elif isinstance(dict_1[key], dict):
copy_dict = copy.deepcopy(dict_1[key])
new_dict[key] = copy_dict
else:
msg = "Error: using unsupported type for key {}: {}".format(
key, type(dict_1[key])
)
print(msg)
raise ValueError(msg)
# Add the unique keys to the new dict
for key in unique_to_dict_2:
# If these items are numerical type, we should add the negative of the value
if isinstance(dict_2[key], ALLOWED_NUMERICAL_TYPES):
new_dict[key] = -dict_2[key]
if new_dict[key] == 0:
del new_dict[key]
# Else we should place the negative of that dictionary in the new place
elif isinstance(dict_2[key], dict):
new_dict[key] = subtract_dicts({}, dict_2[key])
else:
msg = "Error: using unsupported type for key {}: {}".format(
key, type(dict_2[key])
)
print(msg)
raise ValueError(msg)
# Go over the common keys:
for key in overlapping_keys:
# See whether the types are actually the same
if not type(dict_1[key]) is type(dict_2[key]):
# Exceptions:
if (type(dict_1[key]) in ALLOWED_NUMERICAL_TYPES) and (
type(dict_2[key]) in ALLOWED_NUMERICAL_TYPES
):
# We can safely subtract the values since they are all numeric
new_dict[key] = dict_1[key] - dict_2[key]
if new_dict[key] == 0:
del new_dict[key]
else:
print(
"Error key: {} value: {} type: {} and key: {} value: {} type: {} are not of the same type and cannot be merged".format(
key,
dict_1[key],
type(dict_1[key]),
key,
dict_2[key],
type(dict_2[key]),
)
)
raise ValueError
# This is where the keys are the same
else:
# If these items are numeric types
if isinstance(dict_1[key], ALLOWED_NUMERICAL_TYPES):
new_dict[key] = dict_1[key] - dict_2[key]
# Remove entry if the value is 0
if new_dict[key] == 0:
del new_dict[key]
# Else, to be safe we should deepcopy them
elif isinstance(dict_1[key], dict):
new_dict[key] = subtract_dicts(dict_1[key], dict_2[key])
# Remove entry if it results in an empty dict
# TODO: write test to prevent empty dicts from showing up
if not new_dict[key]:
del new_dict[key]
else:
msg = "Error: using unsupported type for key {}: {}".format(
key, type(dict_2[key])
)
print(msg)
raise ValueError(msg)
#
return new_dict
def get_moe_distefano_dataset(options):
"""
Function to get the default moe and Distefano dataset or accept a userinput.
"""
if not options.get("file", None):
print("Using the default Moe and de Stefano 2017 datafile")
json_data = copy.deepcopy(moe_distefano_data.moe_distefano_2017_data)
else:
if not os.path.isfile(options["file"]):
print(
"The provided 'file' Moe and de Stefano JSON file does not seem to exist at {}".format(
options["file"]
)
)
raise ValueError
if not options["file"].endswith(".json"):
print("Provided filename does not end with .json")
else:
# Read input data and Clean up the data if there are whitespaces around the keys
with open(options["file"], "r") as data_filehandle:
datafile_data = data_filehandle.read()
datafile_data = datafile_data.replace('" ', '"')
datafile_data = datafile_data.replace(' "', '"')
datafile_data = datafile_data.replace(' "', '"')
json_data = json.loads(datafile_data)
return json_data
def imports():
for name, val in globals().items():
if isinstance(val, types.ModuleType):
yield val.__name__
class catchtime(object):
"""
Context manager to calculate time spent
"""
def __enter__(self):
"""On entry we start the clock"""
self.t = time.clock()
return self
def __exit__(self, type, value, traceback):
"""On exit we stop the clock and measure the time spent"""
self.t = time.clock() - self.t
print("Took {}s".format(self.t))
def is_capsule(o):
"""
Function to tell whether object is a capsule
"""
t = type(o)
return t.__module__ == "builtins" and t.__name__ == "PyCapsule"
class Capturing(list):
"""
Context manager to capture output and store it
"""
def __enter__(self):
"""On entry we capture the stdout output"""
self._stdout = sys.stdout
sys.stdout = self._stringio = StringIO()
return self
def __exit__(self, *args):
"""On exit we release the capture again"""
self.extend(self._stringio.getvalue().splitlines())
del self._stringio # free up some memory
sys.stdout = self._stdout
########################################################
# utility functions
########################################################
def verbose_print(message: str, verbosity: int, minimal_verbosity: int) -> None:
"""
Function that decides whether to print a message based on the current verbosity
and its minimum verbosity
if verbosity is equal or higher than the minimum, then we print
Args:
message: message to print
verbosity: current verbosity level
minimal_verbosity: threshold verbosity above which to print
"""
if verbosity >= minimal_verbosity:
print(message)
def remove_file(file: str, verbosity: int = 0) -> None:
"""
Function to remove files but with verbosity
Args:
file: full filepath to the file that will be removed.
verbosity: current verbosity level (Optional)
Returns:
the path of a subdirectory called binary_c_python in the TMP of the filesystem
"""
if os.path.exists(file):
if not os.path.isfile(file):
verbose_print(
"This path ({}) is a directory, not a file".format(file), verbosity, 0
)
try:
verbose_print("Removed {}".format(file), verbosity, 1)
os.remove(file)
except FileNotFoundError as inst:
print("Error while deleting file {}: {}".format(file, inst))
else:
verbose_print(
"File/directory {} doesn't exist. Can't remove it.".format(file),
verbosity,
1,
)
def temp_dir(*args: str) -> str:
"""
Function to create directory within the TMP directory of the filesystem
Makes use of os.makedirs exist_ok which requires python 3.2+
Args:
function arguments: str input where each next input will be a child of the previous full_path. e.g. temp_dir('tests', 'grid') will become '/tmp/binary_c_python/tests/grid'
Returns:
the path of a subdirectory called binary_c_python in the TMP of the filesystem
"""
tmp_dir = tempfile.gettempdir()
path = os.path.join(tmp_dir, "binary_c_python")
# loop over the other paths if there are any:
if args:
for extra_dir in args:
path = os.path.join(path, extra_dir)
#
os.makedirs(path, exist_ok=True)
return path
def create_hdf5(data_dir: str, name: str) -> None:
"""
Function to create an hdf5 file from the contents of a directory:
- settings file is selected by checking on files ending on settings
- data files are selected by checking on files ending with .dat
TODO: fix missing settingsfiles
Args:
data_dir: directory containing the data files and settings file
name: name of hdf5file.
"""
# Make HDF5:
# Create the file
hdf5_filename = os.path.join(data_dir, "{}".format(name))
print("Creating {}".format(hdf5_filename))
hdf5_file = h5py.File(hdf5_filename, "w")
# Get content of data_dir
content_data_dir = os.listdir(data_dir)
# Settings
if any([file.endswith("_settings.json") for file in content_data_dir]):
print("Adding settings to HDF5 file")
settings_file = os.path.join(
data_dir,
[file for file in content_data_dir if file.endswith("_settings.json")][0],
)
with open(settings_file, "r") as settings_file:
settings_json = json.load(settings_file)
# Create settings group
settings_grp = hdf5_file.create_group("settings")
# Write version_string to settings_group
settings_grp.create_dataset("used_settings", data=json.dumps(settings_json))
# Get data files
data_files = [el for el in content_data_dir if el.endswith(".dat")]
if data_files:
print("Adding data to HDF5 file")
# Create the data group
data_grp = hdf5_file.create_group("data")
# Write the data to the file:
# Make sure:
for data_file in data_files:
# filename stuff
filename = data_file
full_path = os.path.join(data_dir, filename)
base_name = os.path.splitext(os.path.basename(filename))[0]
# Get header info
header_name = "{base_name}_header".format(base_name=base_name)
data_headers = np.genfromtxt(full_path, dtype="str", max_rows=1)
data_headers = np.char.encode(data_headers)
data_grp.create_dataset(header_name, data=data_headers)
# Add data
data = np.loadtxt(full_path, skiprows=1)
data_grp.create_dataset(base_name, data=data)
hdf5_file.close()
########################################################
# version_info functions
########################################################
def return_binary_c_version_info(parsed: bool = False) -> Union[str, dict]:
"""
Function that returns the version information of binary_c. This function calls the function
_binary_c_bindings.return_version_info()
Args:
parsed: Boolean flag whether to parse the version_info output of binary_c. default = False
Returns:
Either the raw string of binary_c or a parsed version of this in the form of a nested
dictionary
"""
found_prev = False
if "BINARY_C_MACRO_HEADER" in os.environ:
# the envvar is already present. lets save that and put that back later
found_prev = True
prev_value = os.environ["BINARY_C_MACRO_HEADER"]
#
os.environ["BINARY_C_MACRO_HEADER"] = "macroxyz"
# Get version_info
version_info = _binary_c_bindings.return_version_info().strip()
# parse if wanted
if parsed:
version_info = parse_binary_c_version_info(version_info)
# delete value
del os.environ["BINARY_C_MACRO_HEADER"]
# put stuff back if we found a previous one
if found_prev:
os.environ["BINARY_C_MACRO_HEADER"] = prev_value
return version_info
def parse_binary_c_version_info(version_info_string: str) -> dict:
"""
Function that parses the binary_c version info. Long function with a lot of branches
TODO: fix this function. stuff is missing: isotopes, macros, nucleosynthesis_sources
Args:
version_info_string: raw output of version_info call to binary_c
Returns:
Parsed version of the version info, which is a dictionary containing the keys: 'isotopes' for isotope info, 'argpairs' for argument pair info (TODO: explain), 'ensembles' for ensemble settings/info, 'macros' for macros, 'elements' for atomic element info, 'DTlimit' for (TODO: explain), 'nucleosynthesis_sources' for nucleosynthesis sources, and 'miscellaneous' for all those that were not caught by the previous groups. 'git_branch', 'git_build', 'revision' and 'email' are also keys, but its clear what those contain.
"""
version_info_dict = {}
# Clean data and put in correct shape
splitted = version_info_string.strip().splitlines()
cleaned = {el.strip() for el in splitted if not el == ""}
##########################
# Network:
# Split off all the networks and parse the info.
networks = {el for el in cleaned if el.startswith("Network ")}
cleaned = cleaned - networks
networks_dict = {}
for el in networks:
network_dict = {}
split_info = el.split("Network ")[-1].strip().split("==")
network_number = int(split_info[0])
network_dict["network_number"] = network_number
network_info_split = split_info[1].split(" is ")
shortname = network_info_split[0].strip()
network_dict["shortname"] = shortname
if not network_info_split[1].strip().startswith(":"):
network_split_info_extra = network_info_split[1].strip().split(":")
longname = network_split_info_extra[0].strip()
network_dict["longname"] = longname
implementation = (
network_split_info_extra[1].strip().replace("implemented in", "")
)
if implementation:
network_dict["implemented_in"] = implementation.strip().split()
networks_dict[network_number] = copy.deepcopy(network_dict)
version_info_dict["networks"] = networks_dict if networks_dict else None
##########################
# Isotopes:
# Split off
isotopes = {el for el in cleaned if el.startswith("Isotope ")}
cleaned = cleaned - isotopes
isotope_dict = {}
for el in isotopes:
split_info = el.split("Isotope ")[-1].strip().split(" is ")
isotope_info = split_info[-1]
name = isotope_info.split(" ")[0].strip()
# Get details
mass_g = float(
isotope_info.split(",")[0].split("(")[1].split("=")[-1][:-2].strip()
)
mass_amu = float(
isotope_info.split(",")[0].split("(")[-1].split("=")[-1].strip()
)
mass_mev = float(
isotope_info.split(",")[-3].split("=")[-1].replace(")", "").strip()
)
A = int(isotope_info.split(",")[-1].strip().split("=")[-1].replace(")", ""))
Z = int(isotope_info.split(",")[-2].strip().split("=")[-1])
#
isotope_dict[int(split_info[0])] = {
"name": name,
"Z": Z,
"A": A,
"mass_mev": mass_mev,
"mass_g": mass_g,
"mass_amu": mass_amu,
}
version_info_dict["isotopes"] = isotope_dict if isotope_dict else None
##########################
# Argpairs:
# Split off
argpairs = set([el for el in cleaned if el.startswith("ArgPair")])
cleaned = cleaned - argpairs
argpair_dict = {}
for el in sorted(argpairs):
split_info = el.split("ArgPair ")[-1].split(" ")
if not argpair_dict.get(split_info[0], None):
argpair_dict[split_info[0]] = {split_info[1]: split_info[2]}
else:
argpair_dict[split_info[0]][split_info[1]] = split_info[2]
version_info_dict["argpairs"] = argpair_dict if argpair_dict else None
##########################
# ensembles:
# Split off
ensembles = {el for el in cleaned if el.startswith("Ensemble")}
cleaned = cleaned - ensembles
ensemble_dict = {}
for el in ensembles:
split_info = el.split("Ensemble ")[-1].split(" is ")
if len(split_info) > 1:
ensemble_dict[int(split_info[0])] = split_info[-1]
version_info_dict["ensembles"] = ensemble_dict if ensemble_dict else None
##########################
# macros:
# Split off
macros = {el for el in cleaned if el.startswith("macroxyz")}
cleaned = cleaned - macros
param_type_dict = {
"STRING": str,
"FLOAT": float,
"MACRO": str,
"INT": int,
"LONG_INT": int,
"UINT": int,
}
macros_dict = {}
for el in macros:
split_info = el.split("macroxyz ")[-1].split(" : ")
param_type = split_info[0]
new_split = "".join(split_info[1:]).split(" is ")
param_name = new_split[0]
param_value = " is ".join(new_split[1:])
# Sometimes the macros have extra information behind it. Needs an update in outputting by binary_c
try:
macros_dict[param_name] = param_type_dict[param_type](param_value)
except ValueError:
macros_dict[param_name] = str(param_value)
version_info_dict["macros"] = macros_dict if macros_dict else None
##########################
# Elements:
# Split off:
elements = {el for el in cleaned if el.startswith("Element")}
cleaned = cleaned - elements
# Fill dict:
elements_dict = {}
for el in elements:
split_info = el.split("Element ")[-1].split(" : ")
name_info = split_info[0].split(" is ")
# get isotope info
isotopes = {}
if not split_info[-1][0] == "0":
isotope_string = split_info[-1].split(" = ")[-1]
isotopes = {
int(split_isotope.split("=")[0]): split_isotope.split("=")[1]
for split_isotope in isotope_string.split(" ")
}
elements_dict[int(name_info[0])] = {
"name": name_info[-1],
"atomic_number": int(name_info[0]),
"amt_isotopes": len(isotopes),
"isotopes": isotopes,
}
version_info_dict["elements"] = elements_dict if elements_dict else None
##########################
# dt_limits:
# split off
dt_limits = {el for el in cleaned if el.startswith("DTlimit")}
cleaned = cleaned - dt_limits
# Fill dict
dt_limits_dict = {}
for el in dt_limits:
split_info = el.split("DTlimit ")[-1].split(" : ")
dt_limits_dict[split_info[1].strip()] = {
"index": int(split_info[0]),
"value": float(split_info[-1]),
}
version_info_dict["dt_limits"] = dt_limits_dict if dt_limits_dict else None
##########################
# Nucleosynthesis sources:
# Split off
nucsyn_sources = {el for el in cleaned if el.startswith("Nucleosynthesis")}
cleaned = cleaned - nucsyn_sources
# Fill dict
nucsyn_sources_dict = {}
for el in nucsyn_sources:
split_info = el.split("Nucleosynthesis source")[-1].strip().split(" is ")
nucsyn_sources_dict[int(split_info[0])] = split_info[-1]
version_info_dict["nucleosynthesis_sources"] = (
nucsyn_sources_dict if nucsyn_sources_dict else None
)
##########################
# miscellaneous:
# All those that I didnt catch with the above filters. Could try to get some more out though.
# TODO: filter a bit more.
misc_dict = {}
# Filter out git revision
git_revision = [el for el in cleaned if el.startswith("git revision")]
misc_dict["git_revision"] = (
git_revision[0].split("git revision ")[-1].replace('"', "")
)
cleaned = cleaned - set(git_revision)
# filter out git url
git_url = [el for el in cleaned if el.startswith("git URL")]
misc_dict["git_url"] = git_url[0].split("git URL ")[-1].replace('"', "")
cleaned = cleaned - set(git_url)
# filter out version
version = [el for el in cleaned if el.startswith("Version")]
misc_dict["version"] = str(version[0].split("Version ")[-1])
cleaned = cleaned - set(version)
git_branch = [el for el in cleaned if el.startswith("git branch")]
misc_dict["git_branch"] = git_branch[0].split("git branch ")[-1].replace('"', "")
cleaned = cleaned - set(git_branch)
build = [el for el in cleaned if el.startswith("Build")]
misc_dict["build"] = build[0].split("Build: ")[-1].replace('"', "")
cleaned = cleaned - set(build)
email = [el for el in cleaned if el.startswith("Email")]
misc_dict["email"] = email[0].split("Email ")[-1].split(",")
cleaned = cleaned - set(email)
other_items = set([el for el in cleaned if " is " in el])
cleaned = cleaned - other_items
for el in other_items:
split = el.split(" is ")
key = split[0].strip()
val = " is ".join(split[1:]).strip()
misc_dict[key] = val
misc_dict["uncaught"] = list(cleaned)
version_info_dict["miscellaneous"] = misc_dict if misc_dict else None
return version_info_dict
########################################################
# binary_c output functions
########################################################
def output_lines(output: str) -> list:
"""
Function that outputs the lines that were recieved from the binary_c run, but now as an iterator.
Args:
output: raw binary_c output
Returns:
Iterator over the lines of the binary_c output
"""
if output:
return output.splitlines()
return []
def example_parse_output(output: str, selected_header: str) -> dict:
"""
Function that parses output of binary_c. This version serves as an example and is quite
detailed. Custom functions can be easier:
This function works in two cases:
if the caught line contains output like 'example_header time=12.32 mass=0.94 ..'
or if the line contains output like 'example_header 12.32 0.94'
Please dont the two cases.
You can give a 'selected_header' to catch any line that starts with that.
Then the values will be put into a dictionary.
Tasks:
- TODO: Think about exporting to numpy array or pandas instead of a defaultdict
- TODO: rethink whether this function is necessary at all
- TODO: check this function again
Args:
output: binary_c output string
selected_header: string header of the output (the start of the line that you want to
process)
Returns:
dictionary containing parameters as keys and lists for the values
"""
value_dicts = []
# split output on newlines
for line in output.split("\n"):
# Skip any blank lines
if not line == "":
split_line = line.split()
# Select parts
header = split_line[0]
values_list = split_line[1:]
# print(values_list)
# Catch line starting with selected header
if header == selected_header:
# Check if the line contains '=' symbols:
value_dict = {}
if all("=" in value for value in values_list):
for value in values_list:
key, val = value.split("=")
value_dict[key.strip()] = val.strip()
value_dicts.append(value_dict)
else:
if any("=" in value for value in values_list):
raise ValueError(
"Caught line contains some = symbols but not \
all of them do. aborting run"
)
for j, val in enumerate(values_list):
value_dict[j] = val
value_dicts.append(value_dict)
if len(value_dicts) == 0:
print(
"Sorry, didnt find any line matching your header {}".format(selected_header)
)
return None
keys = value_dicts[0].keys()
# Construct final dict.
final_values_dict = defaultdict(list)
for value_dict in value_dicts:
for key in keys:
final_values_dict[key].append(value_dict[key])
return final_values_dict
########################################################
# Argument and default value functions
########################################################
def get_defaults(filter_values: bool = False) -> dict:
"""
Function that calls the binaryc get args function and cast it into a dictionary.
All the values are strings
Args:
filter_values: whether to filter out NULL and Function defaults.
Returns:
dictionary containing the parameter name as key and the parameter default as value
"""
default_output = _binary_c_bindings.return_arglines()
default_dict = {}
for default in default_output.split("\n"):
if not default in ["__ARG_BEGIN", "__ARG_END", ""]:
key, value = default.split(" = ")
default_dict[key] = value
if filter_values:
default_dict = filter_arg_dict(default_dict)
return default_dict
def get_arg_keys() -> list:
"""
Function that return the list of possible keys to give in the arg string.
This function calls get_defaults()
Returns:
list of all the parameters that binary_c accepts (and has default values for, since
we call get_defaults())
"""
return list(get_defaults().keys())
def filter_arg_dict(arg_dict: dict) -> dict:
"""
Function to filter out keys that contain values included in ['NULL', 'Function', '']
This function is called by get_defaults()
Args:
arg_dict: dictionary containing the argument + default keypairs of binary_c
Returns:
filtered dictionary (pairs with NULL and Function values are removed)
"""
old_dict = arg_dict.copy()
new_dict = {}
for key in old_dict.keys():
if not old_dict[key] in ["NULL", "Function"]:
if not old_dict[key] == "":
new_dict[key] = old_dict[key]
return new_dict
def create_arg_string(
arg_dict: dict, sort: bool = False, filter_values: bool = False
) -> str:
"""
Function that creates the arg string for binary_c. Takes a dictionary containing the arguments
and writes them to a string
This string is missing the 'binary_c ' at the start.
Args:
arg_dict: dictionary
sort: (optional, default = False) Boolean whether to sort the order of the keys.
filter_values: (optional, default = False) filters the input dict on keys that have NULL or `function` as value.
Returns:
The string built up by combining all the key + value's.
"""
arg_string = ""
# Whether to filter the arguments
if filter_values:
arg_dict = filter_arg_dict(arg_dict)
#
keys = sorted(arg_dict.keys()) if sort else arg_dict.keys()
#
for key in keys:
arg_string += "{key} {value} ".format(key=key, value=arg_dict[key])
arg_string = arg_string.strip()
return arg_string
########################################################
# Help functions
########################################################
def get_help(
param_name: str = "", print_help: bool = True, fail_silently: bool = False
) -> Union[dict, None]:
"""
Function that returns the help info for a given parameter, by interfacing with binary_c
Will check whether it is a valid parameter.
Binary_c will output things in the following order;
- Did you mean?
- binary_c help for variable
- default
- available macros
This function reads out that structure and catches the different components of this output
Tasks:
- TODO: consider not returning None, but return empty dict
Args:
param_name: name of the parameter that you want info from. Will get checked whether its a
valid parameter name
print_help: (optional, default = True) whether to print out the help information
fail_silently: (optional, default = False) Whether to print the errors raised if the
parameter isn't valid
Returns:
Dictionary containing the help info. This dictionary contains 'parameter_name',
'parameter_value_input_type', 'description', optionally 'macros'
"""
available_arg_keys = get_arg_keys()
if not param_name:
print(
"Please set the param_name to any of the following:\n {}".format(
sorted(available_arg_keys)
)
)
return None
if param_name in available_arg_keys:
help_info = _binary_c_bindings.return_help(param_name)
cleaned = [el for el in help_info.split("\n") if not el == ""]
# Get line numbers
did_you_mean_nr = [
i for i, el in enumerate(cleaned) if el.startswith("Did you mean")
]
parameter_line_nr = [
i for i, el in enumerate(cleaned) if el.startswith("binary_c help")
]
default_line_nr = [
i for i, el in enumerate(cleaned) if el.startswith("Default")
]
macros_line_nr = [
i for i, el in enumerate(cleaned) if el.startswith("Available")
]
help_info_dict = {}
# Get alternatives
if did_you_mean_nr:
alternatives = cleaned[did_you_mean_nr[0] + 1 : parameter_line_nr[0]]
alternatives = [el.strip() for el in alternatives]
help_info_dict["alternatives"] = alternatives
# Information about the parameter
parameter_line = cleaned[parameter_line_nr[0]]
parameter_name = parameter_line.split(":")[1].strip().split(" ")[0]
parameter_value_input_type = (
" ".join(parameter_line.split(":")[1].strip().split(" ")[1:])
.replace("<", "")
.replace(">", "")
)
help_info_dict["parameter_name"] = parameter_name
help_info_dict["parameter_value_input_type"] = parameter_value_input_type
description_line = " ".join(
cleaned[parameter_line_nr[0] + 1 : default_line_nr[0]]
)
help_info_dict["description"] = description_line
# Default:
default_line = cleaned[default_line_nr[0]]
default_value = default_line.split(":")[-1].strip()
help_info_dict["default"] = default_value
# Get Macros:
if macros_line_nr:
macros = cleaned[macros_line_nr[0] + 1 :]
help_info_dict["macros"] = macros
if print_help:
for key in help_info_dict:
print("{}:\n\t{}".format(key, help_info_dict[key]))
return help_info_dict
else:
if not fail_silently:
print(
"{} is not a valid parameter name. Please choose from the \
following parameters:\n\t{}".format(
param_name, list(available_arg_keys)
)
)
return None
def get_help_all(print_help: bool = True) -> dict:
"""
Function that reads out the output of the return_help_all api call to binary_c. This return_help_all binary_c returns all the information for the parameters, their descriptions and other properties. The output is categorized in sections.
Args:
print_help: (optional, default = Tru) prints all the parameters and their descriptions.
Returns:
returns a dictionary containing dictionaries per section. These dictionaries contain the parameters and descriptions etc for all the parameters in that section
"""
# Call function
help_all = _binary_c_bindings.return_help_all()
# String manipulation
split = help_all.split(
"############################################################\n"
)
cleaned = [el for el in split if not el == "\n"]
section_nums = [i for i in range(len(cleaned)) if cleaned[i].startswith("#####")]
# Create dicts
help_all_dict = {}
# Select the section name and the contents of that section. Note, not all sections have content!
for i in range(len(section_nums)):
if not i == len(section_nums) - 1:
params = cleaned[section_nums[i] + 1 : section_nums[i + 1]]
else:
params = cleaned[section_nums[i] + 1 : len(cleaned)]
section_name = (
cleaned[section_nums[i]]
.lstrip("#####")
.strip()
.replace("Section ", "")
.lower()
)
#
params_dict = {}
if params:
# Clean it, replace in-text newlines with a space and then split on newlines.
split_params = params[0].strip().replace("\n ", " ").split("\n")
# Process params and descriptions per section
for split_param in split_params:
split_param_info = split_param.split(" : ")
if not len(split_param_info) == 3:
# there are ocassions where the semicolon
# is used in the description text itself.
if len(split_param_info) == 4:
split_param_info = [
split_param_info[0],
": ".join([split_param_info[1], split_param_info[2]]),
split_param_info[3],
]
# other occassions?
# Put the information in a dict
param_name = split_param_info[0]
param_description = split_param_info[1]
if len(split_param_info) > 2:
rest = split_param_info[2:]
else:
rest = None
params_dict[param_name] = {
"param_name": param_name,
"description": param_description,
"rest": "".join(rest) if rest else "",
}
# make section_dict
section_dict = {
"section_name": section_name,
"parameters": params_dict.copy(),
}
# Put in the total dict
help_all_dict[section_name] = section_dict.copy()
# Print things
if print_help:
for section in sorted(help_all_dict.keys()):
print(
"##################\n###### Section {}\n##################".format(
section
)
)
section_dict = help_all_dict[section]
for param_name in sorted(section_dict["parameters"].keys()):
param = section_dict["parameters"][param_name]
print(
"\n{}:\n\t{}: {}".format(
param["param_name"], param["description"], param["rest"]
)
)
# # Loop over all the parameters an call the help() function on it.
# # Takes a long time but this is for testing
# for section in help_all_dict.keys():
# section_dict = help_all_dict[section]
# for param in section_dict['parameters'].keys():
# get_help(param)
return help_all_dict
def get_help_super(print_help: bool = False, fail_silently: bool = True) -> dict:
"""
Function that first runs get_help_all, and then per argument also run
the help function to get as much information as possible.
Args:
print_help: (optional, default = False) Whether to print the information
fail_silently: (optional, default = True) Whether to fail silently or to print the errors
Returns:
dictionary containing all dictionaries per section, which then contain as much info as possible per parameter.
"""
# Get help_all information
help_all_dict = get_help_all(print_help=False)
for section_name in help_all_dict:
section = help_all_dict[section_name]
# print(section_name)
# for parameter_name in section["parameters"].keys():
# print("\t", parameter_name)
help_all_super_dict = help_all_dict.copy()
# Loop over all sections and stuff
for section_name in help_all_dict:
# Skipping the section i/o because that one shouldn't be available to python anyway
if not section_name == "i/o":
section = help_all_dict[section_name]
for parameter_name in section["parameters"].keys():
parameter = section["parameters"][parameter_name]
# Get detailed help info
detailed_help = get_help(
parameter_name,
print_help=False,
fail_silently=fail_silently,
)
if detailed_help:
# check whether the descriptions of help_all and detailed help are the same
if not fail_silently:
if not parameter["description"] == detailed_help["description"]:
print(json.dumps(parameter, indent=4))
## put values into help all super dict
# input type
parameter["parameter_value_input_type"] = detailed_help[
"parameter_value_input_type"
]
# default
parameter["default"] = detailed_help["default"]
# macros
if "macros" in detailed_help.keys():
parameter["macros"] = detailed_help["macros"]
section["parameters"][parameter_name] = parameter
if print_help:
print(json.dumps(help_all_super_dict, indent=4))
return help_all_super_dict
def make_build_text() -> str:
"""
Function to make build text
Returns:
string containing information about the build and the git branch
"""
version_info = return_binary_c_version_info(parsed=True)
git_revision = version_info["miscellaneous"]["git_revision"]
git_branch = version_info["miscellaneous"]["git_branch"]
build_datetime = version_info["miscellaneous"]["build"]
info_string = """
This information was obtained by the following binary_c build:
\t**binary_c git branch**: {}\t**binary_c git revision**: {}\t**Built on**: {}
""".format(
git_branch, git_revision, build_datetime
)
return info_string.strip()
def write_binary_c_parameter_descriptions_to_rst_file(output_file: str) -> None:
"""
Function that calls the get_help_super() to get the help text/descriptions for all the
parameters available in that build.
Writes the results to a .rst file that can be included in the docs.
Tasks:
- TODO: add the specific version git branch, git build, git commit, and binary_c version to
this document
Args:
output_file: name of the output .rst faile containing the ReStructuredText formatted output
of all the binary_c parameters.
"""
# Get the whole arguments dictionary
arguments_dict = get_help_super()
build_info = make_build_text()
if not output_file.endswith(".rst"):
print("Filename doesn't end with .rst, please provide a proper filename")
return None
with open(output_file, "w") as f:
print("Binary\\_c parameters", file=f)
print("{}".format("=" * len("Binary\\_c parameters")), file=f)
print(
"The following chapter contains all the parameters that the current version of binary\\_c can handle, along with their descriptions and other properties.",
file=f,
)
print("\n", file=f)
print(build_info, file=f)
print("\n", file=f)
for el in arguments_dict.keys():
print("Section: {}".format(el), file=f)
print("{}\n".format("-" * len("Section: {}".format(el))), file=f)
# print(arguments_dict[el]['parameters'].keys())
for arg in arguments_dict[el]["parameters"].keys():
argdict = arguments_dict[el]["parameters"][arg]
print("| **Parameter**: {}".format(argdict["param_name"]), file=f)
print("| **Description**: {}".format(argdict["description"]), file=f)
if "parameter_value_input_type" in argdict:
print(
"| **Parameter input type**: {}".format(
argdict["parameter_value_input_type"]
),
file=f,
)
if "default" in argdict:
print("| **Default value**: {}".format(argdict["default"]), file=f)
if "macros" in argdict:
print("| **Macros**: {}".format(argdict["macros"]), file=f)
if not argdict["rest"] == "(null)":
print("| **Extra**: {}".format(argdict["rest"]), file=f)
print("", file=f)
########################################################
# logfile functions
########################################################
def load_logfile(logfile: str) -> None:
"""
Experimental function that parses the generated logfile of binary_c.
This function is not finished and shouldn't be used yet.
Tasks:
- TODO:
Args:
- logfile: filename of the logfile you want to parse
Returns:
"""
with open(logfile, "r") as file:
logfile_data = file.readlines()
time_list = []
m1_list = []
m2_list = []
k1_list = []
k2_list = []
sep_list = []
ecc_list = []
rel_r1_list = []
rel_r2_list = []
event_list = []
# random_seed = logfile_data[0].split()[-2]
# random_count = logfile_data[0].split()[-1]
# probability = logfile_data[-1].split()
for line in logfile_data[1:-1]:
split_line = line.split()
time_list.append(split_line[0])
m1_list.append(split_line[1])
m2_list.append(split_line[2])
k1_list.append(split_line[3])
k2_list.append(split_line[4])
sep_list.append(split_line[5])
ecc_list.append(split_line[6])
rel_r1_list.append(split_line[7])
rel_r2_list.append(split_line[8])
event_list.append(" ".join(split_line[9:]))
print(event_list)
########################################################
# Ensemble dict functions
########################################################
def inspect_dict(
input_dict: dict, indent: int = 0, print_structure: bool = True
) -> dict:
"""
Function to (recursively) inspect a (nested) dictionary.
The object that is returned is a dictionary containing the key of the input_dict, but as value
it will return the type of what the value would be in the input_dict
In this way we inspect the structure of these dictionaries, rather than the exact contents.
Args:
input_dict: dictionary you want to inspect
print_structure: (optional, default = True)
indent: (optional, default = 0) indent of the first output
Returns:
Dictionary that has the same structure as the input_dict, but as values it has the
type(input_dict[key]) (except if the value is a dict)
"""
structure_dict = {}
#
for key, value in input_dict.items():
structure_dict[key] = type(value)
if print_structure:
print("\t" * indent, key, type(value))
if isinstance(value, dict):
structure_dict[key] = inspect_dict(
value, indent=indent + 1, print_structure=print_structure
)
return structure_dict
def merge_dicts(dict_1: dict, dict_2: dict) -> dict:
"""
Function to merge two dictionaries in a custom way.
Behaviour:
When dict keys are only present in one of either:
- we just add the content to the new dict
When dict keys are present in both, we decide based on the value types how to combine them:
- dictionaries will be merged by calling recursively calling this function again
- numbers will be added
- (opt) lists will be appended
- In the case that the instances do not match: for now I will raise an error
Args:
dict_1: first dictionary
dict_2: second dictionary
Returns:
Merged dictionary
"""
# Set up new dict
new_dict = {}
#
keys_1 = dict_1.keys()
keys_2 = dict_2.keys()
# Find overlapping keys of both dicts
overlapping_keys = set(keys_1).intersection(set(keys_2))
# Find the keys that are unique
unique_to_dict_1 = set(keys_1).difference(set(keys_2))
unique_to_dict_2 = set(keys_2).difference(set(keys_1))
# Add the unique keys to the new dict
for key in unique_to_dict_1:
# If these items are ints or floats, then just put them in
if isinstance(dict_1[key], (float, int)):
new_dict[key] = dict_1[key]
# Else, to be safe we should deepcopy them
else:
copy_dict = copy.deepcopy(dict_1[key])
new_dict[key] = copy_dict
for key in unique_to_dict_2:
# If these items are ints or floats, then just put them in
if isinstance(dict_2[key], (float, int)):
new_dict[key] = dict_2[key]
# Else, to be safe we should deepcopy them
else:
copy_dict = copy.deepcopy(dict_2[key])
new_dict[key] = copy_dict
# Go over the common keys:
for key in overlapping_keys:
# See whether the types are actually the same
if not type(dict_1[key]) is type(dict_2[key]):
# Exceptions:
if (type(dict_1[key]) in [int, float, np.float64]) and (
type(dict_2[key]) in [int, float, np.float64]
):
new_dict[key] = dict_1[key] + dict_2[key]
else:
print(
"Error key: {} value: {} type: {} and key: {} value: {} type: {} are not of the same type and cannot be merged".format(
key,
dict_1[key],
type(dict_1[key]),
key,
dict_2[key],
type(dict_2[key]),
)
)
raise ValueError
# Here we check for the cases that we want to explicitly catch. Ints will be added,
# floats will be added, lists will be appended (though that might change) and dicts will be
# dealt with by calling this function again.
else:
# ints
# Booleans (has to be the type Bool, not just a 0 or 1)
if isinstance(dict_1[key], bool) and isinstance(dict_2[key], bool):
new_dict[key] = dict_1[key] or dict_2[key]
elif isinstance(dict_1[key], int) and isinstance(dict_2[key], int):
new_dict[key] = dict_1[key] + dict_2[key]
# floats
elif isinstance(dict_1[key], float) and isinstance(dict_2[key], float):
new_dict[key] = dict_1[key] + dict_2[key]
# lists
elif isinstance(dict_1[key], list) and isinstance(dict_2[key], list):
new_dict[key] = dict_1[key] + dict_2[key]
# dicts
elif isinstance(dict_1[key], dict) and isinstance(dict_2[key], dict):
new_dict[key] = merge_dicts(dict_1[key], dict_2[key])
else:
print(
"Object types {}: {} ({}), {} ({}) not supported.".format(
key,
dict_1[key],
type(dict_1[key]),
dict_2[key],
type(dict_2[key]),
)
)
raise ValueError
#
return new_dict
def update_dicts(dict_1: dict, dict_2: dict) -> dict:
"""
Function to update dict_1 with values of dict_2 in a recursive way.
Behaviour:
When dict keys are only present in one of either:
- we just add the content to the new dict
When dict keys are present in both, we decide based on the value types how to combine them:
- value of dict2 will be taken
Args:
dict_1: first dictionary
dict_2: second dictionary
Returns:
New dictionary with Updated values
"""
# Set up new dict
new_dict = {}
#
keys_1 = dict_1.keys()
keys_2 = dict_2.keys()
# Find overlapping keys of both dicts
overlapping_keys = set(keys_1).intersection(set(keys_2))
# Find the keys that are unique
unique_to_dict_1 = set(keys_1).difference(set(keys_2))
unique_to_dict_2 = set(keys_2).difference(set(keys_1))
# Add the unique keys to the new dict
for key in unique_to_dict_1:
# If these items are ints or floats, then just put them in
if isinstance(dict_1[key], (float, int)):
new_dict[key] = dict_1[key]
# Else, to be safe we should deepcopy them
else:
copy_dict = copy.deepcopy(dict_1[key])
new_dict[key] = copy_dict
for key in unique_to_dict_2:
# If these items are ints or floats, then just put them in
if isinstance(dict_2[key], (float, int)):
new_dict[key] = dict_2[key]
# Else, to be safe we should deepcopy them
else:
copy_dict = copy.deepcopy(dict_2[key])
new_dict[key] = copy_dict
# Go over the common keys:
for key in overlapping_keys:
# See whether the types are actually the same
if not type(dict_1[key]) is type(dict_2[key]):
# Exceptions:
if (type(dict_1[key]) in [int, float]) and (
type(dict_2[key]) in [int, float]
):
new_dict[key] = dict_2[key]
else:
print(
"Error key: {} value: {} type: {} and key: {} value: {} type: {} are not of the same type and cannot be merged".format(
key,
dict_1[key],
type(dict_1[key]),
key,
dict_2[key],
type(dict_2[key]),
)
)
raise ValueError
# Here we check for the cases that we want to explicitly catch. Ints will be added,
# floats will be added, lists will be appended (though that might change) and dicts will be
# dealt with by calling this function again.
else:
# dicts
if isinstance(dict_1[key], dict) and isinstance(dict_2[key], dict):
new_dict[key] = update_dicts(dict_1[key], dict_2[key])
else:
new_dict[key] = dict_2[key]
#
return new_dict
def extract_ensemble_json_from_string(binary_c_output: str) -> dict:
"""
Function to extract the ensemble_json information from a raw binary_c output string
Args:
binary_c_output: raw binary_c output string
Returns:
json dictionary with the parsed ENSEMBLE_JSON data
"""
json_dict = None
try:
# If there is no output just return an empty dict:
if not binary_c_output:
json_dict = {}
return json_dict
ensemble_jsons_strings = [
line
for line in binary_c_output.splitlines()
if line.startswith("ENSEMBLE_JSON")
]
json_dict = handle_ensemble_string_to_json(
ensemble_jsons_strings[0][len("ENSEMBLE_JSON ") :]
)
if len(ensemble_jsons_strings) > 1:
verbose_print(
"Warning: There is more than one line starting with ENSEMBLE_JSON. Taking the first, but you should check this out.",
1,
0,
)
except IndexError:
verbose_print(
"Error: Couldn't extract the ensemble information from the output string",
0,
0,
)
return json_dict
class binarycDecoder(json.JSONDecoder):
"""
Custom decoder to transform the numbers that are strings to actual floats
"""
def decode(self, s):
"""
Entry point function for decoding
"""
result = super().decode(
s
) # result = super(Decoder, self).decode(s) for Python 2.x
return self._decode(result)
def _decode(self, o):
"""
Depending on the type of object is will determine whether to loop over the elements,
or try to change the type of the object from string to float
The try except might be a somewhat rough solution but it catches all cases.
"""
# Check if we can turn it into a float
# if isinstance(o, str) or isinstance(o, unicode):
if isinstance(o, str):
try:
return float(o)
except ValueError:
return o
elif isinstance(o, dict):
return {k: self._decode(v) for k, v in o.items()}
elif isinstance(o, list):
return [self._decode(v) for v in o]
else:
return o
class BinaryCEncoder(json.JSONEncoder):
"""
Encoding class function to attempt to convert things to strings.
"""
def default(self, o):
"""
Converting function. Well, could be more precise. look at the json module
"""
try:
str_repr = str(o)
except TypeError:
pass
else:
return str_repr
# Let the base class default method raise the TypeError
return json.JSONEncoder.default(self, o)
def binaryc_json_serializer(obj: Any) -> Any:
"""
Custom serializer for binary_c to use when functions are present in the dictionary
that we want to export.
Function objects will be turned into str representations of themselves
Args:
obj: obj being process
Returns:
Either string representation of object if the object is a function, or the object itself
"""
if inspect.isfunction(obj) or isinstance(obj, py_rinterpolate.Rinterpolate):
return str(obj)
return obj
def handle_ensemble_string_to_json(raw_output):
"""
Function that deals with the raw output of the ensemble and
creates a working JSON dictionary out of it.
Having this wrapper makes it easy to
Args:
raw_output: raw output of the ensemble dump by binary_c
Returns:
json.loads(raw_output, cls=binarycDecoder)
"""
# return json.loads(json.dumps(ast.literal_eval(raw_output)), cls=binarycDecoder)
return json.loads(raw_output, cls=binarycDecoder)